Theincreasingcomplexityofspacevehiclessuchassatellites,
andthecostreduction measures that have affected satellite operators
are increasingly driving the need for more autonomy in satellite
diagnostics and control systems. Current methods for detecting and
correcting anomalies onboard the spacecraft as well as on the
ground are primarily manual and labor intensive, and therefore,
tend to be slow. Operators inspect telemetry data to determine the
current satellite health. They use various statisticaltechniques
andmodels, buttheanalysisandevaluation ofthelargevolume of data
still require extensive human intervention and expertise that is
prone to error. Furthermore, for spacecraft and most of these
satellites, there can be potentially unduly long delays in
round-trip communications between the ground station and the
satellite. In this context, it is desirable to have onboard
fault-diagnosis system that is capable of detecting, isolating,
identifying or classifying faults in the system
withouttheinvolvementandinterventionofoperators.Towardthisend,
theprinciple goal here is to improve the ef?ciency, accuracy, and
reliability of the trend analysis and diagnostics techniques
through utilization of intelligent-based and hybrid-based
methodologi